def default_transforms(self) -> Dict[str, Callable]:
if self.training:
post_tensor_transform = [
RandomShortSideScale(min_size=256, max_size=320),
RandomCrop(244),
RandomHorizontalFlip(p=0.5),
]
else:
post_tensor_transform = [
ShortSideScale(256),
]
return {
"post_tensor_transform": Compose([
ApplyTransformToKey(
key="video",
transform=Compose([UniformTemporalSubsample(8)] + post_tensor_transform),
),
]),
"per_batch_transform_on_device": Compose([
ApplyTransformToKey(
key="video",
transform=K.VideoSequential(
K.Normalize(torch.tensor([0.45, 0.45, 0.45]), torch.tensor([0.225, 0.225, 0.225])),
data_format="BCTHW",
same_on_frame=False
)
),
]),
}
def test_compose_with_video_transforms(self):
video = thwc_to_cthw(create_dummy_video_frames(
20, 30, 40)).to(dtype=torch.float32)
test_clip = {"video": video, "label": 0}
# Compose using torchvision and pytorchvideo transformst to ensure they interact
# correctly.
num_subsample = 10
transform = Compose([
ApplyTransformToKey(
key="video",
transform=Compose([
UniformTemporalSubsample(num_subsample),
NormalizeVideo([video.mean()] * 3, [video.std()] * 3),
RandomShortSideScale(min_size=15, max_size=25),
RandomCropVideo(10),
RandomHorizontalFlipVideo(p=0.5),
]),
)
])
actual = transform(test_clip)
c, t, h, w = actual["video"].shape
self.assertEqual(c, 3)
self.assertEqual(t, num_subsample)
self.assertEqual(h, 10)
self.assertEqual(w, 10)
def get_tfms(self):
tfms_list = [
UniformTemporalSubsample(self.transform_params["num_frames"]),
Lambda(lambda x: x / 255.0),
Normalize(self.mean, self.std),
]
if self.resize:
tfms_list += [
ShortSideScale(size=self.transform_params["side_size"]),
CenterCropVideo(crop_size=(self.transform_params["crop_size"],
self.transform_params["crop_size"]))
]
# Note that this transform is specific to the x3d model.
tfms = ApplyTransformToKey(
key="video",
transform=Compose(tfms_list),
)
# The duration of the input clip is also specific to the model.
clip_duration = (
self.transform_params["num_frames"] *
self.transform_params["sampling_rate"]) / self.frames_per_second
return tfms, clip_duration
def _video_transform(self, mode: str):
"""
This function contains example transforms using both PyTorchVideo and TorchVision
in the same Callable. For 'train' mode, we use augmentations (prepended with
'Random'), for 'val' mode we use the respective determinstic function.
"""
args = self.args
return ApplyTransformToKey(
key="video",
transform=Compose(
[
UniformTemporalSubsample(args.video_num_subsampled),
Normalize(args.video_means, args.video_stds),
]
+ (
[
RandomShortSideScale(
min_size=args.video_min_short_side_scale,
max_size=args.video_max_short_side_scale,
),
RandomCrop(args.video_crop_size),
RandomHorizontalFlip(p=args.video_horizontal_flip_p),
]
if mode == "train"
else [
ShortSideScale(args.video_min_short_side_scale),
CenterCrop(args.video_crop_size),
]
)
),
)
def per_sample_transform(self) -> Callable:
per_sample_transform = [CenterCrop(self.image_size)]
return ApplyToKeys(
"video",
Compose([
UniformTemporalSubsample(self.temporal_sub_sample), normalize
] + per_sample_transform),
)
def train_per_sample_transform(self) -> Callable:
per_sample_transform = [
RandomCrop(self.image_size, pad_if_needed=True)
]
return ApplyToKeys(
"video",
Compose([
UniformTemporalSubsample(self.temporal_sub_sample), normalize
] + per_sample_transform),
)
def __init__(self,
train_paths,
val_paths,
clip_duration: int = 2,
batch_size: int = 4,
num_workers: int = 2,
**kwargs):
super().__init__()
self.train_paths = train_paths
self.val_paths = val_paths
self.batch_size = batch_size
self.num_workers = num_workers
self.clip_duration = clip_duration
self.num_labels = len(
{path[1]
for path in train_paths._paths_and_labels})
for k, v in kwargs.items():
setattr(self, k, v)
self.train_transforms = ApplyTransformToKey(
key='video',
transform=Compose([
UniformTemporalSubsample(8),
Lambda(lambda x: x / 255.0),
Normalize((0.45, 0.45, 0.45), (0.225, 0.225, 0.225)),
RandomShortSideScale(min_size=256, max_size=320),
RandomCrop(224),
RandomHorizontalFlip(p=0.5),
]))
self.val_transforms = ApplyTransformToKey(
key='video',
transform=Compose([
UniformTemporalSubsample(8),
Lambda(lambda x: x / 255.0),
Normalize((0.45, 0.45, 0.45), (0.225, 0.225, 0.225)),
ShortSideScale(256),
CenterCrop(224)
]))
def get_transform():
transform = Compose([
ApplyTransformToKey(
key="video",
transform=Compose([
UniformTemporalSubsample(8),
#Normalize((0.45, 0.45, 0.45), (0.225, 0.225, 0.225)),
RandomShortSideScale(min_size=256, max_size=320),
RandomCrop(244),
RandomHorizontalFlip(p=0.5),
]),
),
])
return transform
def test_torchscriptable_input_output(self):
video = thwc_to_cthw(create_dummy_video_frames(20, 30, 40)).to(
dtype=torch.float32
)
# Test all the torchscriptable tensors.
for transform in [UniformTemporalSubsample(10), RandomShortSideScale(10, 20)]:
transform_script = torch.jit.script(transform)
self.assertTrue(isinstance(transform_script, torch.jit.ScriptModule))
# Seed before each transform to force determinism.
torch.manual_seed(0)
output = transform(video)
torch.manual_seed(0)
script_output = transform_script(video)
self.assertTrue(output.equal(script_output))
def _audio_transform(self):
"""
This function contains example transforms using both PyTorchVideo and TorchAudio
in the same Callable.
"""
args = self.args
n_fft = int(
float(args.audio_resampled_rate) / 1000 * args.audio_mel_window_size
)
hop_length = int(
float(args.audio_resampled_rate) / 1000 * args.audio_mel_step_size
)
eps = 1e-10
return ApplyTransformToKey(
key="audio",
transform=Compose(
[
Resample(
orig_freq=args.audio_raw_sample_rate,
new_freq=args.audio_resampled_rate,
),
MelSpectrogram(
sample_rate=args.audio_resampled_rate,
n_fft=n_fft,
hop_length=hop_length,
n_mels=args.audio_num_mels,
center=False,
),
Lambda(lambda x: x.clamp(min=eps)),
Lambda(torch.log),
UniformTemporalSubsample(args.audio_mel_num_subsample),
Lambda(lambda x: x.transpose(1, 0)), # (F, T) -> (T, F)
Lambda(
lambda x: x.view(1, x.size(0), 1, x.size(1))
), # (T, F) -> (1, T, 1, F)
Normalize((args.audio_logmel_mean,), (args.audio_logmel_std,)),
]
),
)
def test_video_classifier_finetune_fiftyone(tmpdir):
with mock_encoded_video_dataset_folder(tmpdir) as (
dir_name,
total_duration,
):
half_duration = total_duration / 2 - 1e-9
train_dataset = fo.Dataset.from_dir(
dir_name,
dataset_type=fo.types.VideoClassificationDirectoryTree,
)
datamodule = VideoClassificationData.from_fiftyone(
train_dataset=train_dataset,
clip_sampler="uniform",
clip_duration=half_duration,
video_sampler=SequentialSampler,
decode_audio=False,
)
for sample in datamodule.train_dataset.data:
expected_t_shape = 5
assert sample["video"].shape[1] == expected_t_shape
assert len(VideoClassifier.available_backbones()) > 5
train_transform = {
"post_tensor_transform": Compose([
ApplyTransformToKey(
key="video",
transform=Compose([
UniformTemporalSubsample(8),
RandomShortSideScale(min_size=256, max_size=320),
RandomCrop(244),
RandomHorizontalFlip(p=0.5),
]),
),
]),
"per_batch_transform_on_device": Compose([
ApplyTransformToKey(
key="video",
transform=K.VideoSequential(
K.Normalize(torch.tensor([0.45, 0.45, 0.45]), torch.tensor([0.225, 0.225, 0.225])),
K.augmentation.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),
data_format="BCTHW",
same_on_frame=False
)
),
]),
}
datamodule = VideoClassificationData.from_fiftyone(
train_dataset=train_dataset,
clip_sampler="uniform",
clip_duration=half_duration,
video_sampler=SequentialSampler,
decode_audio=False,
train_transform=train_transform
)
model = VideoClassifier(num_classes=datamodule.num_classes, pretrained=False)
trainer = flash.Trainer(fast_dev_run=True)
trainer.finetune(model, datamodule=datamodule)
def Ptvkinetics(cfg, mode):
"""
Construct the Kinetics video loader with a given csv file. The format of
the csv file is:
```
path_to_video_1 label_1
path_to_video_2 label_2
...
path_to_video_N label_N
```
For `train` and `val` mode, a single clip is randomly sampled from every video
with random cropping, scaling, and flipping. For `test` mode, multiple clips are
uniformaly sampled from every video with center cropping.
Args:
cfg (CfgNode): configs.
mode (string): Options includes `train`, `val`, or `test` mode.
For the train and val mode, the data loader will take data
from the train or val set, and sample one clip per video.
For the test mode, the data loader will take data from test set,
and sample multiple clips per video.
"""
# Only support train, val, and test mode.
assert mode in [
"train",
"val",
"test",
], "Split '{}' not supported".format(mode)
logger.info("Constructing Ptvkinetics {}...".format(mode))
clip_duration = (cfg.DATA.NUM_FRAMES * cfg.DATA.SAMPLING_RATE /
cfg.DATA.TARGET_FPS)
path_to_file = os.path.join(cfg.DATA.PATH_TO_DATA_DIR,
"{}.csv".format(mode))
labeled_video_paths = LabeledVideoPaths.from_path(path_to_file)
num_videos = len(labeled_video_paths)
labeled_video_paths.path_prefix = cfg.DATA.PATH_PREFIX
logger.info("Constructing kinetics dataloader (size: {}) from {}".format(
num_videos, path_to_file))
if mode in ["train", "val"]:
num_clips = 1
num_crops = 1
transform = Compose([
ApplyTransformToKey(
key="video",
transform=Compose([
UniformTemporalSubsample(cfg.DATA.NUM_FRAMES),
Lambda(div255),
NormalizeVideo(cfg.DATA.MEAN, cfg.DATA.STD),
RandomShortSideScale(
min_size=cfg.DATA.TRAIN_JITTER_SCALES[0],
max_size=cfg.DATA.TRAIN_JITTER_SCALES[1],
),
RandomCropVideo(cfg.DATA.TRAIN_CROP_SIZE),
] + ([RandomHorizontalFlipVideo(
p=0.5)] if cfg.DATA.RANDOM_FLIP else []) +
[PackPathway(cfg)]),
),
DictToTuple(num_clips, num_crops),
])
clip_sampler = make_clip_sampler("random", clip_duration)
if cfg.NUM_GPUS > 1:
video_sampler = DistributedSampler
else:
video_sampler = (RandomSampler
if mode == "train" else SequentialSampler)
else:
num_clips = cfg.TEST.NUM_ENSEMBLE_VIEWS
num_crops = cfg.TEST.NUM_SPATIAL_CROPS
transform = Compose([
ApplyTransformToKey(
key="video",
transform=Compose([
UniformTemporalSubsample(cfg.DATA.NUM_FRAMES),
Lambda(div255),
NormalizeVideo(cfg.DATA.MEAN, cfg.DATA.STD),
ShortSideScale(size=cfg.DATA.TRAIN_JITTER_SCALES[0]),
]),
),
UniformCropVideo(size=cfg.DATA.TEST_CROP_SIZE),
ApplyTransformToKey(key="video", transform=PackPathway(cfg)),
DictToTuple(num_clips, num_crops),
])
clip_sampler = make_clip_sampler(
"constant_clips_per_video",
clip_duration,
num_clips,
num_crops,
)
video_sampler = (DistributedSampler
if cfg.NUM_GPUS > 1 else SequentialSampler)
return PTVDatasetWrapper(
num_videos=num_videos,
clips_per_video=num_clips,
crops_per_clip=num_crops,
dataset=LabeledVideoDataset(
labeled_video_paths=labeled_video_paths,
clip_sampler=clip_sampler,
video_sampler=video_sampler,
transform=transform,
decode_audio=False,
),
)
from torchvision.transforms import CenterCrop, Compose, RandomCrop, RandomHorizontalFlip
else:
print("Please, run `pip install torchvideo kornia`")
sys.exit(1)
if __name__ == '__main__':
# 1. Download a video clip dataset. Find more dataset at https://pytorchvideo.readthedocs.io/en/latest/data.html
download_data("https://pl-flash-data.s3.amazonaws.com/kinetics.zip")
# 2. [Optional] Specify transforms to be used during training.
# Flash helps you to place your transform exactly where you want.
# Learn more at:
# https://lightning-flash.readthedocs.io/en/latest/general/data.html#flash.core.data.process.Preprocess
post_tensor_transform = [
UniformTemporalSubsample(8),
RandomShortSideScale(min_size=256, max_size=320)
]
per_batch_transform_on_device = [
K.Normalize(torch.tensor([0.45, 0.45, 0.45]),
torch.tensor([0.225, 0.225, 0.225]))
]
train_post_tensor_transform = post_tensor_transform + [
RandomCrop(244), RandomHorizontalFlip(p=0.5)
]
val_post_tensor_transform = post_tensor_transform + [CenterCrop(244)]
train_per_batch_transform_on_device = per_batch_transform_on_device
def make_transform(
post_tensor_transform: List[Callable] = post_tensor_transform,
for k, v in kinetics_classnames.items():
kinetics_id_to_classname[v] = str(k).replace('"', "")
# Input Transform
# slow_r50モデルに固有なパラメータであることに注意！！
side_size = 256
mean = [0.45, 0.45, 0.45]
std = [0.225, 0.225, 0.225]
crop_size = 256
num_frames = 8
sampling_rate = 8
frames_per_second = 30
transform = ApplyTransformToKey(key='video',
transform=Compose([
UniformTemporalSubsample(num_frames),
Lambda(lambda x: x / 255.0),
NormalizeVideo(mean, std),
ShortSideScale(size=side_size),
CenterCropVideo(crop_size=(crop_size,
crop_size))
]))
clip_duration = (num_frames * sampling_rate) / frames_per_second
# Load Video
video_path = 'archery.mp4'
start_sec = 0
end_sec = start_sec + clip_duration
video = EncodedVideo.from_path(video_path)
"x3d_m": {
"side_size": 256,
"crop_size": 256,
"num_frames": 16,
"sampling_rate": 5,
}
}
# Get transform parameters based on model
transform_params = model_transform_params[model_name]
# Note that this transform is specific to the slow_R50 model.
transform = ApplyTransformToKey(
key="video",
transform=Compose([
UniformTemporalSubsample(transform_params["num_frames"]),
Lambda(lambda x: x / 255.0),
NormalizeVideo(mean, std),
ShortSideScale(size=transform_params["side_size"]),
CenterCropVideo(crop_size=(transform_params["crop_size"],
transform_params["crop_size"]))
]),
)
# The duration of the input clip is also specific to the model.
clip_duration = (transform_params["num_frames"] *
transform_params["sampling_rate"]) / frames_per_second
def x3dpred(video):
